




"""simple docstring"""





import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding



class   a__					(						__magic_name__ ):
			lowercase_		=						["image_processor", "tokenizer"]
			lowercase_		=						"ChineseCLIPImageProcessor"
			lowercase_		=						("BertTokenizer", "BertTokenizerFast")


			def __init__(   self   :     Tuple   ,	UpperCamelCase_   :     List[str]=None   ,	UpperCamelCase_   :     Union[str, Any]=None   ,	**UpperCamelCase_   :     str):


						"""simple docstring"""




						__UpperCAmelCase						:			Union[str, Any]												=       None
						if "feature_extractor" in kwargs:
									warnings.warn(
									    "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
									    " instead."   ,	UpperCamelCase_   ,	)
									__UpperCAmelCase						:			Union[str, Any]												=       kwargs.pop("feature_extractor")

						__UpperCAmelCase						:			Union[str, Any]												=       image_processor if image_processor is not None else feature_extractor
						if image_processor is None:
									raise ValueError("You need to specify an `image_processor`.")
						if tokenizer is None:
									raise ValueError("You need to specify a `tokenizer`.")

						super().__init__(UpperCamelCase_   ,	UpperCamelCase_)
						__UpperCAmelCase						:			List[str]												=       self.image_processor


			def __call__(   self   :     Any   ,	UpperCamelCase_   :     Optional[Any]=None   ,	UpperCamelCase_   :     Dict=None   ,	UpperCamelCase_   :     Any=None   ,	**UpperCamelCase_   :     Optional[Any]):


						"""simple docstring"""




						if text is None and images is None:
									raise ValueError("You have to specify either text or images. Both cannot be none.")

						if text is not None:
									__UpperCAmelCase						:			Dict												=       self.tokenizer(UpperCamelCase_   ,	return_tensors=UpperCamelCase_   ,	**UpperCamelCase_)

						if images is not None:
									__UpperCAmelCase						:			Any												=       self.image_processor(UpperCamelCase_   ,	return_tensors=UpperCamelCase_   ,	**UpperCamelCase_)

						if text is not None and images is not None:
									__UpperCAmelCase						:			Any												=       image_features.pixel_values
									return encoding
						elif text is not None:
									return encoding
						else:
									return BatchEncoding(data=dict(**UpperCamelCase_)   ,	tensor_type=UpperCamelCase_)


			def    a_  (   self   :     List[str]   ,	*UpperCamelCase_   :     Union[str, Any]   ,	**UpperCamelCase_   :     Optional[int]):


						"""simple docstring"""




						return self.tokenizer.batch_decode(*UpperCamelCase_   ,	**UpperCamelCase_)


			def    a_  (   self   :     Dict   ,	*UpperCamelCase_   :     str   ,	**UpperCamelCase_   :     Any):


						"""simple docstring"""




						return self.tokenizer.decode(*UpperCamelCase_   ,	**UpperCamelCase_)


			@property
			def    a_  (   self   :     str):


						"""simple docstring"""




						__UpperCAmelCase						:			Any												=       self.tokenizer.model_input_names
						__UpperCAmelCase						:			Tuple												=       self.image_processor.model_input_names
						return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names))


			@property
			def    a_  (   self   :     Optional[Any]):


						"""simple docstring"""




						warnings.warn(
						    "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."   ,	UpperCamelCase_   ,	)
						return self.image_processor_class




from __future__ import annotations

def a__       (			A_,	A_,	A_,	A_   ):






  '''simple docstring'''





  if (direction == 1 and array[indexa] > array[indexa]) or (
      direction == 0 and array[indexa] < array[indexa]
  ):
    __magic_name__	,       __magic_name__								=			array[indexa], array[indexa]

def a__       (			A_,	A_,	A_,	A_   ):






  '''simple docstring'''





  if length > 1:
    __magic_name__								=			int(length / 2   )
    for i in range(A_,	low + middle   ):
      comp_and_swap(A_,	A_,	i + middle,	A_   )
    bitonic_merge(A_,	A_,	A_,	A_   )
    bitonic_merge(A_,	low + middle,	A_,	A_   )

def a__       (			A_,	A_,	A_,	A_   ):






  '''simple docstring'''





  if length > 1:
    __magic_name__								=			int(length / 2   )
    bitonic_sort(A_,	A_,	A_,	1   )
    bitonic_sort(A_,	low + middle,	A_,	0   )
    bitonic_merge(A_,	A_,	A_,	A_   )


if __name__ == "__main__":
    __lowerCAmelCase     :	Optional[Any]       			=  input('Enter numbers separated by a comma:\n').strip()
    __lowerCAmelCase     :	List[Any]       			=  [int(item.strip()) for item in user_input.split(',')]

    bitonic_sort(unsorted, 0, len(unsorted), 1)
    print('\nSorted array in ascending order is: ', end='')
    print(*unsorted, sep=', ')

    bitonic_merge(unsorted, 0, len(unsorted), 0)
    print('Sorted array in descending order is: ', end='')
    print(*unsorted, sep=', ')

